Occlusion Robust Light Field Depth Estimation Using Segmentation Guided Bilateral Filtering

Abstract

Although light field data provides abundant cues for depth estimation, light field depth estimation suffers from occlusion and uncertain edges. In this paper, we propose occlusion robust light field depth estimation using segmentation guided bilateral filtering. First, we calculate refocused images from light field data using digital refocusing. Second, we perform support vector machines (SVM) classification to classify occluded pixels and non-occluded pixels. Third, we conduct different cost estimations on occluded and non-occluded pixels, and remove noise by cost volume filtering. Finally, we perform segmentation-guided bilateral filtering to refine the depth map while preserving edges. Experimental results on both synthetic and our own data sets demonstrate that the proposed method achieves light field depth estimation robust to occlusion while successfully preserving edges.